Skip to main content

nteract/dx — efficient display and blob-store uploads from Python kernels

Project description

dx

Smart DataFrame display for Jupyter, built for nteract.

dx upgrades how pandas and polars DataFrames render in a notebook. Instead of serializing megabytes of HTML into your output cells, dx hands the data to nteract's content-addressed blob store and renders it through a fast Arrow/parquet grid. Your .ipynb stays tiny, the cell stays snappy, and AI agents reading the notebook get a compact per-column summary — dtypes, ranges, distinct/top values, null counts — instead of raw bytes.

Install

# pandas
pip install "dx[pandas]"

# polars
pip install "dx[polars]"

# both
pip install "dx[pandas,polars]"

Python 3.10+.

Use

import dx
dx.install()

import pandas as pd
df = pd.read_parquet("large-dataset.parquet")
df  # rendered via nteract's sift grid — no base64 in your .ipynb

That's it. dx.install() is idempotent and automatically called by nteract's kernel bootstrap, so most nteract users never touch it directly. Calling it yourself is fine when you want the behavior in an environment nteract didn't configure for you (a standalone kernel, a test harness, etc.).

What you get

  • Fast rendering. Large DataFrames stream through the blob store; the .ipynb payload stays small.
  • AI-friendly summaries. Every DataFrame ships a text/llm+plain column summary — dtypes, numeric ranges, string distinct/top values, null counts — so agents reason about the shape without materializing the whole table.
  • Visualization integration. Altair and Plotly are automatically switched to their nteract renderers for interactive output that works inside nteract's isolated iframe sandbox.
  • Narwhals-aware. narwhals-wrapped DataFrames are unwrapped via .to_native() and dispatched through the pandas/polars path.
  • Safe outside nteract. When no nteract runtime is reachable, dx.install() is a no-op. import dx is safe from plain Python, vanilla Jupyter, scripts, CI.

Links

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dx-2.0.1a202604180056.tar.gz (23.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

dx-2.0.1a202604180056-py3-none-any.whl (16.3 kB view details)

Uploaded Python 3

File details

Details for the file dx-2.0.1a202604180056.tar.gz.

File metadata

  • Download URL: dx-2.0.1a202604180056.tar.gz
  • Upload date:
  • Size: 23.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: uv/0.11.7 {"installer":{"name":"uv","version":"0.11.7","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

File hashes

Hashes for dx-2.0.1a202604180056.tar.gz
Algorithm Hash digest
SHA256 5cad5f5fd6c2e05f7d53a4bd6106d7cfdf68116db13e6691cf4dfdb5678504f7
MD5 84ead9e5f93540617d7ded406adfcc67
BLAKE2b-256 2c36f8f5b8758cf987bf445f4a8e10ebd49c3846d98f9b0d2d0eebdd5073dc36

See more details on using hashes here.

File details

Details for the file dx-2.0.1a202604180056-py3-none-any.whl.

File metadata

  • Download URL: dx-2.0.1a202604180056-py3-none-any.whl
  • Upload date:
  • Size: 16.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: uv/0.11.7 {"installer":{"name":"uv","version":"0.11.7","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

File hashes

Hashes for dx-2.0.1a202604180056-py3-none-any.whl
Algorithm Hash digest
SHA256 a59dc79fe23bec8dfcb631f2bc2b592a8c46b36cb9db6dd9685bc97cf8ca49e0
MD5 cf02e576259f46ad57ec57ce79e988b3
BLAKE2b-256 c7c9f2bc2f4538b6908d4c582c1cdcd3b87eb7fa25f199574fd8c3f7a4094394

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page